Abstract

Motion pose capture technology can effectively solve the problem of difficulty in defining character motion in the process of 3D animation production and greatly reduce the workload of character motion control, thereby improving the efficiency of animation development and the fidelity of character motion. Motion gesture capture technology is widely used in virtual reality systems, virtual training grounds, and real-time tracking of the motion trajectories of general objects. This paper proposes an attitude estimation algorithm adapted to be embedded. The previous centralized Kalman filter is divided into two-step Kalman filtering. According to the different characteristics of the sensors, they are processed separately to isolate the cross-influence between sensors. An adaptive adjustment method based on fuzzy logic is proposed. The acceleration, angular velocity, and geomagnetic field strength of the environment are used as the input of fuzzy logic to judge the motion state of the carrier and then adjust the covariance matrix of the filter. The adaptive adjustment of the sensor is converted to the recognition of the motion state. For the study of human motion posture capture, this paper designs a verification experiment based on the existing robotic arm in the laboratory. The experiment shows that the studied motion posture capture method has better performance. The human body motion gesture is designed for capturing experiments, and the capture results show that the obtained pose angle information can better restore the human body motion. A visual model of human motion posture capture was established, and after comparing and analyzing with the real situation, it was found that the simulation approach reproduced the motion process of human motion well. For the research of human motion recognition, this paper designs a two-classification model and human daily behaviors for experiments. Experiments show that the accuracy of the two-category human motion gesture capture and recognition has achieved good results. The experimental effect of SVC on the recognition of two classifications is excellent. In the case of using all optimization algorithms, the accuracy rate is higher than 90%, and the final recognition accuracy rate is also higher than 90%. In terms of recognition time, the time required for human motion gesture capture and recognition is less than 2 s.